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stochastic model

The Stocky is the main forecasting tool driving the analysis on this site. It’s a simulator of the season ahead, using the Monte Carlo method and based on Elo ratings, that gives insight into the future performance of each club. My main interest has been the number of wins, as it determines ladder positions which in turn have a big impact on the finals. The Stocky might not be able to tell you which games a team will win, but it is good at telling you how many wins are ahead.

But how does a computer simulation (in reality, a very large spreadsheet) compare to reality? To test it, I’ve put together a graph of each team’s performance against what the Stocky projected for them. Each graph shows:

The Stocky’s projection for total wins (blue)

Converting that projection to a “pace” for that point in the season (red)

Comparing that to the actual number of wins (yellow)

It will never be exactly right, particularly as you can only ever win whole numbers of games and the Stocky loves a decimal point, but as we’ll see, the Stocky is not too bad at tracking form and projecting that forward.

This week is Part II, from North Queensland to Wests Tigers. Part I, from Brisbane to Newcastle, was last week. Also see this week’s projections update for some errors in the Stocky.

The Stocky is the main forecasting tool driving the analysis on this site. It’s a simulator of the season ahead, using the Monte Carlo method and based on Elo ratings, that gives insight into the future performance of each club. My main interest has been the number of wins, as it determines ladder positions which in turn have a big impact on the finals. The Stocky might not be able to tell you which games a team will win, but it is good at telling you how many wins are ahead.

But how does a computer simulation (in reality, a very large spreadsheet) compare to reality? To test it, I’ve put together a graph of each team’s performance against what the Stocky projected for them. Each graph shows:

The Stocky’s projection for total wins (blue)

Converting that projection to a “pace” for that point in the season (red)

Comparing that to the actual number of wins (yellow)

It will never be exactly right, particularly as you can only ever win whole numbers of games and the Stocky loves a decimal point, but as we’ll see, the Stocky is not too bad at tracking form and projecting that forward.

This week is Part I, from Brisbane to Newcastle. Part II, from North Queensland to Wests Tigers, will be next week.

The basic premise of a Monte Carlo simulation is that if you have a few pieces of the puzzle, an idea of how they relate and then throw enough random numbers at it, you’ll get a pretty good idea of what the puzzle picture is.

Let’s say you have a circle inside a square with sides the same length as the circle’s diameter. Then throw a bunch of sand onto the square/circle combination and count how many grains of sand end up in the circle. If you know the length of the square’s side and the proportion of sand that ends up in the circle, you can work out a value for π.

(You want more detail? Fine: the side of the square can be used to calculate the area of the square, multiply that by the proportion of sand inside the circle will give you an estimate of the circle’s area, divide the circle’s area by square of half the square’s length and you will get an estimate of π).

The more grains of sand you throw at the square/circle, the closer the estimate will be to the actual answer.